CGIAR Platform for Big Data in Agriculture

Digital agricultural technology in Egypt: Insights from app developers

Digital technologies have rapidly reshaped agricultural systems worldwide, and Egypt is no exception. Over the past decade, the proliferation of smartphones, mobile internet, and low-cost digital tools has opened new channels through which farmers, traders, processors, and aggregators access information and services. Smartphone applications, websites, call centers, and SMS-based platforms now offer advice on crop management, weather and climate alerts, input and output price information, traceability tools, and digital marketplaces. For smallholder farmers—who make up the backbone of Egypt`s agricultural sector—these tools have the potential to reduce information frictions, improve decision-making, and increase productivity and profitability. For larger firms engaged in aggregation, processing, or export, digital platforms can streamline supply chains, enhance coordination, and improve quality assurance.

Adolescents in South Asian agrifood systems: Roles and aspirations

Ethical use of artificial intelligence in food, land and water systems research: A guide for equity and inclusion

This document aims to support CGIAR researchers and their partners by providing guidance, recommendations and resources on ethical considerations in using artificial intelligence (AI) in food, land and water systems (FLWS) research and practice with attention to equity and inclusion dimensions. This toolkit complements the GENDER Impact Platform Ethics and Standards Toolkit and aligns with CGIAR’s core ethical values outlined in the CGIAR Ethics Framework and the CGIAR Research Ethics Code. In response to the rapid development and adoption of emerging AI technologies, the toolkit presents key issues and practical recommendations for researchers to consider across the research cycle, drawing on a comprehensive literature review and interviews with experts in AI ethics and social inclusion. AI systems, as used in this context, refer to technologies that mimic aspects of human cognition and behavior—interpreting real-world data, generating predictions, and adapting through continuous learning (Benefo et al. 2022; Fu 2022; Manning et al. 2022). These systems are increasingly central to a suite of AI and agricultural technologies poised to transform FLWS research: not only in how we conduct research and generate insights, but also in how we define best practice for gender-related research and understand the downstream impacts of our research outputs.